Smart greenhouse farming: a review towards near zero energy consumption
Bibliographic record
Abstract
Abstract The global agricultural sector faces increasing challenges in adopting sustainable practices and reducing its environmental footprint. Smart greenhouse agriculture has emerged as a key solution, enabling efficient year-round crop production while minimizing dependence on traditional field farming. However, achieving near-zero energy consumption in greenhouses remains a major challenge due to the high operational energy demands. This review examines the current state of energy consumption in greenhouses, critically analyzes existing technological solutions, and identifies key challenges, such as high energy consumption for heating, cooling, and lighting. The study highlights opportunities for integrating renewable energy sources, optimizing energy-saving systems, and using advanced control technologies such as artificial intelligence (AI) and the Internet of Things (IoT) to monitor microclimatic conditions. Results show that integrating these solutions can significantly reduce energy consumption while maintaining optimal growing environments. The main findings include prioritizing the adoption of hybrid renewable energy systems, improving greenhouse design and material selection, and enhancing real-time monitoring systems with smart technologies. Future research should focus on cost-effective innovations, interdisciplinary approaches, and the scalability of energy-efficient designs. This review provides actionable information for researchers, policymakers, and practitioners to advance the transition to sustainable, near-zero energy greenhouse systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".